Selecting partial task resources in a dispersed storage network

ABSTRACT

A method for use in a dispersed storage network operates to identify a selected DSTE unit of a subset of DSTE units to perform one or more partial tasks of a task to be performed on at least one encoded data slice; issue the one or more partial tasks to the selected DSTE unit; receive one or more partial results from the selected DSTE unit, wherein the selected DSTE unit performs the one or more partial tasks on the at least one encoded data slice to produce the one or more partial results; and output a result based on the received one or more partial task.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/260,735,entitled “ACCESSING COPIES OF DATA STORED IN A DISPERSED STORAGENETWORK”, filed Nov. 30, 2015, which is hereby incorporated herein byreference in its entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of in accordancewith the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public interne systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a DST execution unit and a set of storage units may beinterchangeably referred to as a set of DST execution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm,Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematicencoding, on-line codes, etc.), a data segmenting protocol (e.g., datasegment size, fixed, variable, etc.), and per data segment encodingvalues. The per data segment encoding values include a total, or pillarwidth, number (T) of encoded data slices per encoding of a data segmenti.e., in a set of encoded data slices); a decode threshold number (D) ofencoded data slices of a set of encoded data slices that are needed torecover the data segment; a read threshold number (R) of encoded dataslices to indicate a number of encoded data slices per set to be readfrom storage for decoding of the data segment; and/or a write thresholdnumber (W) to indicate a number of encoded data slices per set that mustbe accurately stored before the encoded data segment is deemed to havebeen properly stored. The dispersed storage error encoding parametersmay further include slicing information (e.g., the number of encodeddata slices that will be created for each data segment) and/or slicesecurity information (e.g., per encoded data slice encryption,compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes distributed storage and task (DST)processing units 1 and 2, the network 24 of FIG. 1, and a set of DSTexecution (DSTE) units 1-8. The processing units 1 and 2 may beimplemented utilizing the computing device 16 of FIG. 1 and can includethe interface 32 of FIG. 1, the computing core 26 of FIG. 1, and the DSclient module 34 of FIG. 1. The processing units 1 or 2 can function asa dispersed storage processing agent for computing device 14 asdescribed previously. Each DSTE unit 1-8 may be implemented utilizingthe DST execution unit 36 of FIG. 1. The DSN functions to process tasksbased on the stored data. In particular, the DSN selects a resource toprocess a partial task.

In an example of operation, the DST processing unit 2 operates toperform a task A. The task A may be a simple function (e.g., amathematical function, a logic function, an identify function, a findfunction, a search engine function, a replace function, etc.), a complexfunction (e.g., compression, human and/or computer language translation,text-to-voice conversion, voice-to-text conversion, etc.), multiplesimple and/or complex functions, one or more algorithms, one or moreapplications, etc. The Task A can be segregated by the DST processingunit 2 into a plurality of partial tasks, that for example, require oneor more slices of data that have been previously stored.

In the example shown, the DSN has previously stored data A as one ormore sets of encoded data slices 1-8 in the DSTE units 1-8. Inparticular, the data A is dispersed storage error encoded with a firstinformation dispersal algorithm (IDA), where each set of encoded dataslices includes a first IDA width (e.g., 8) number of slices, where afirst decode threshold number (e.g., 5) is required to recover the data,and where the first decode threshold number is greater than half of thefirst IDA width number. The DST processing unit 1 also stores the data Ain a subset of the DSTE units 1-3 as another one or more sets of encodeddata slices C1-C3, where the data A is dispersed storage error encodedutilizing a second IDA to produce the one or more other sets of encodeddata slices C1-C3, where each of the other sets of encoded data slicesC1-C3 includes a second IDA width number of slices (e.g., 3), where asecond decode threshold number is required to recover the data, andwhere the second decode threshold number (e.g., 1) is less than or equalto half of the second IDA width number, the DST processing unit 2selects a DSTE unit of the subset of DSTE units to perform one or morepartial tasks of a task to be performed on the data A.

The selecting of the DSTE unit may be based on one or more of an amountof data contained in one slice stored at a DSTE unit, DSTE unitavailability information, a task processing time requirement, a DSTEunit performance level, an interpretation of system registryinformation, and a predetermination. For example, the DST processingunit 2 selects the DSTE unit 3 when unit 3 is available and encoded dataslice C3 includes all of the data A.

Having selected the DSTE unit, the DST processing unit 2 issues one ormore partial tasks to the selected DSTE unit. For example, the DSTprocessing unit 2 generates one or more partial tasks A based on a taskA, sends, via the network 24, the one or more partial tasks A to theselected DSTE unit 3.

Having issued the partial tasks, the DST processing unit 2 receives oneor more partial results from the selected DSTE unit. For example, theDST processing unit 2 receives, via the network 24, one or more partialresults A from the DST unit 3, where the DST unit 3 performs the one ormore partial tasks A on the encoded data slice C3 to produce the partialresults A.

Having received the one or more partial results, the DST processing unit2 outputs a result based on the received one or more partial results.For example, the DST processing unit 2 generates a result A based on thereceived one or more partial results A.

While the DST processing units 1 and 2 are described above inconjunction with the operation of computing unit 16, partial tasksresources may likewise be selected in a similar fashion by integrityprocessing unit 20 and/or managing unit 18 of FIG. 1.

FIG. 10 is a flowchart illustrating an example of selecting a resourceto process a partial task. In particular, a method is presented for usein conjunction with one or more functions and features described inassociation with FIGS. 1-9. For example, the method can be executed by adispersed storage and task (DST) processing unit that includes aprocessor and associated memory or via another processing system of adispersed storage network that includes at least one processor andmemory that stores instruction that configure the processor orprocessors to perform the steps described below. The method includes astep 200 where data has been stored as one or more sets of encoded dataslices in a set of distributed storage and task execution (DSTE) unitsand as another one or more sets of encoded data slices in a subset ofDST units. A DSTE unit of the subset of DSTE units is selected toperform one or more partial tasks of a task to be performed on the data.The data it is dispersed storage error encoded utilizing a firstinformation dispersal algorithm (IDA), where each set of encoded dataslices includes a first IDA width number of slices, where a first decodethreshold number is required to recover the data, where the first decodethreshold number is greater than half of the first IDA width number,where the data is encoded utilizing a second IDA produce the other oneor more sets of encoded data slices, where each of the other sets ofencoded data slices includes a second IDA width number of slices, wherea second decode threshold number is required to recover the data, andwhere the second decode threshold number is less than or equal to halfof the second IDA width number.

The selecting can be based on one or more of an amount of data containedin one slice at the DSTE unit, DSTE unit availability information, atask processing time requirement, a DSTE unit performance level, aninterpretation of system registry information, and a predetermination.For example, the processing module selects a third DSTE unit when thethird DSTE unit is available and an encoded data slice of the other oneor more sets of encoded data slices is stored at the third DST unit andwhen the encoded data slice includes all the data for the performing ofthe partial task.

The method continues at the step 202 where the processing module issuesone or more partial tasks to the selected DSTE unit. For example, theprocessing module generates one or more partial tasks A based on a taskA and sends the one or more partial tasks A to the selected DSTE unit 3.The method continues at the step 204 where the processing modulereceives one or more partial results from the selected DSTE unit, wherethe DSTE unit performs the one or more partial task on the encoded dataslice to produce the one or more partial results. For example, theprocessing module receives one or more partial results A from the DSTEunit 3. The method continues at the step 206 where the processing moduleoutputs a result based on the received one or more partial task. Forexample, the processing module generates a result A based on thereceived one or more partial results A.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to: store data as one or more first sets of encodeddata slices in a plurality of dispersed storage and task execution(DSTE) units, the one or more first sets of encoded data slices encodedutilizing a first information dispersal algorithm having a first decodethreshold number, wherein the data is also stored as one or more secondsets of encoded data slices in a subset of the plurality of DSTE units,the one or more second sets of encoded data slices encoded utilizing asecond information dispersal algorithm having a second decode thresholdnumber; identify a selected DSTE unit of the subset of DSTE units toperform one or more partial tasks of a task to be performed on at leastone encoded data slice of the one or more second sets of encoded dataslices; issue the one or more partial tasks to the selected DSTE unit;receive one or more partial results from the selected DSTE unit, whereinthe selected DSTE unit performs the one or more partial tasks on the atleast one encoded data slice to produce the one or more partial results;and output a result based on the received one or more partial task.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a processing system ofa dispersed storage and task (DST) processing unit that includes aprocessor, the method comprises: storing data as one or more first setsof encoded data slices in a plurality of dispersed storage and taskexecution (DSTE) units, the one or more first sets of encoded dataslices encoded utilizing a first information dispersal algorithm havinga first decode threshold number, wherein the data is also stored as oneor more second sets of encoded data slices in a subset of the pluralityof DSTE units, the one or more second sets of encoded data slicesencoded utilizing a second information dispersal algorithm having asecond decode threshold number; identifying, via the processing system,a selected DSTE unit of the subset of DSTE units to perform one or morepartial tasks of a task to be performed on at least one encoded dataslice of the one or more second sets of encoded data slices; issuing,via the processing system, the one or more partial tasks to the selectedDSTE unit; receiving, via the processing system, one or more partialresults from the selected DSTE unit, wherein the selected DSTE unitperforms the one or more partial tasks on the at least one encoded dataslice to produce the one or more partial results; and outputting, viathe processing system, a result based on the received one or morepartial task results; wherein the each of the one or more first sets ofencoded data slices includes a first information dispersal algorithm(IDA) width number of encoded data slices, and wherein the first decodethreshold number of each of the one or more first sets of encoded dataslices is required to recover the data using the first IDA, and whereinthe first decode threshold number is greater than half of the first IDAwidth number.
 2. The method of claim 1 wherein identifying the selectedDTSE unit is based on one or more of: an amount of data contained in oneslice at the DSTE unit or DSTE unit availability information.
 3. Themethod of claim 1 wherein identifying the selected DTSE unit is based onone or more of: a task processing time requirement or a DSTE unitperformance level.
 4. The method of claim 1 wherein identifying theselected DTSE unit is based on one or more of: an interpretation ofsystem registry information or a predetermination.
 5. The method ofclaim 1 wherein identifying the selected DTSE unit is based on adetermination that the selected DSTE unit is available, that the atleast one encoded data slice is stored at the selected DST unit andfurther that the at least one encoded data slice includes all the datafor performing of the partial task.
 6. The method of claim 1 wherein theeach of the one or more second sets of encoded data slices includes asecond IDA width number of encoded data slices, and wherein the seconddecode threshold number of each of the one or more second sets ofencoded data slices is required to recover the data using the secondIDA, and where the second decode threshold number is less than or equalto half of the second IDA width number.
 7. A processing system of afirst dispersed storage and task (DST) processing unit comprises: atleast one processor; memory that stores operational instructions, thatwhen executed by the at least one processor cause the processing systemto: store data as one or more first sets of encoded data slices in aplurality of dispersed storage and task execution (DSTE) units, the oneor more first sets of encoded data slices encoded utilizing a firstinformation dispersal algorithm having a first decode threshold number,wherein the data is also stored as one or more second sets of encodeddata slices in a subset of the plurality of DSTE units, the one or moresecond sets of encoded data slices encoded utilizing a secondinformation dispersal algorithm having a second decode threshold number;identify a selected DSTE unit of the subset of DSTE units to perform oneor more partial tasks of a task to be performed on at least one encodeddata slice of the one or more second sets of encoded data slices; issuethe one or more partial tasks to the selected DSTE unit; receive one ormore partial results from the selected DSTE unit, wherein the selectedDSTE unit performs the one or more partial tasks on the at least oneencoded data slice to produce the one or more partial results; andoutput a result based on the received one or more partial task results;wherein the each of the one or more first sets of encoded data slicesincludes a first information dispersal algorithm (IDA) width number ofencoded data slices, and wherein the first decode threshold number ofeach of the one or more first sets of encoded data slices is required torecover the data using the first IDA, and wherein the first decodethreshold number is greater than half of the first IDA width number. 8.The processing system of claim 7 wherein identifying the selected DTSEunit is based on one or more of: an amount of data contained in oneslice at the DSTE unit or DSTE unit availability information.
 9. Theprocessing system of claim 7 wherein identifying the selected DTSE unitis based on one or more of: a task processing time requirement or a DSTEunit performance level.
 10. The processing system of claim 7 whereinidentifying the selected DTSE unit is based on one or more of: aninterpretation of system registry information or a predetermination. 11.The processing system of claim 7 wherein identifying the selected DTSEunit is based on a determination that the selected DSTE unit isavailable, that the at least one encoded data slice is stored at theselected DST unit and further that the at least one encoded data sliceincludes all the data for performing of the partial task.
 12. Theprocessing system of claim 7 wherein the each of the one or more secondsets of encoded data slices includes a second IDA width number ofencoded data slices, and wherein the second decode threshold number ofeach of the one or more second sets of encoded data slices is requiredto recover the data using the second IDA, and where the second decodethreshold number is less than or equal to half of the second IDA widthnumber.
 13. A non-transitory computer readable storage medium comprises:at least one memory section that stores operational instructions that,when executed by a processing system of a dispersed storage network(DSN) that includes a processor and a memory, causes the processingsystem to: store data as one or more first sets of encoded data slicesin a plurality of dispersed storage and task execution (DSTE) units, theone or more first sets of encoded data slices encoded utilizing a firstinformation dispersal algorithm having a first decode threshold number,wherein the data is also stored as one or more second sets of encodeddata slices in a subset of the plurality of DSTE units, the one or moresecond sets of encoded data slices encoded utilizing a secondinformation dispersal algorithm having a second decode threshold number;identify a selected DSTE unit of the subset of DSTE units to perform oneor more partial tasks of a task to be performed on at least one encodeddata slice of the one or more second sets of encoded data slices; issuethe one or more partial tasks to the selected DSTE unit; receive one ormore partial results from the selected DSTE unit, wherein the selectedDSTE unit performs the one or more partial tasks on the at least oneencoded data slice to produce the one or more partial results; andoutput a result based on the received one or more partial task results;wherein the each of the one or more first sets of encoded data slicesincludes a first information dispersal algorithm (IDA) width number ofencoded data slices, and wherein the first decode threshold number ofeach of the one or more first sets of encoded data slices is required torecover the data using the first IDA, and wherein the first decodethreshold number is greater than half of the first IDA width number. 14.The non-transitory computer readable storage medium of claim 13 whereinidentifying the selected DTSE unit is based on one or more of: an amountof data contained in one slice at the DSTE unit or DSTE unitavailability information.
 15. The non-transitory computer readablestorage medium of claim 13 wherein identifying the selected DTSE unit isbased on one or more of: a task processing time requirement or a DSTEunit performance level.
 16. The non-transitory computer readable storagemedium of claim 13 wherein identifying the selected DTSE unit is basedon one or more of: an interpretation of system registry information or apredetermination.
 17. The non-transitory computer readable storagemedium of claim 13 wherein identifying the selected DTSE unit is basedon a determination that the selected DSTE unit is available, that the atleast one encoded data slice is stored at the selected DST unit andfurther that the at least one encoded data slice includes all the datafor performing of the partial task.
 18. The non-transitory computerreadable storage medium of claim 13 wherein the each of the one or moresecond sets of encoded data slices includes a second IDA width number ofencoded data slices, and wherein the second decode threshold number ofeach of the one or more second sets of encoded data slices is requiredto recover the data using the second IDA, and where the second decodethreshold number is less than or equal to half of the second IDA widthnumber.